model_analyse: analyses lmer4 and lm models created using help function...

In wolski/prolfqua: Proteomics Label Free Quantification

analyses lmer4 and lm models created using help function 'strategy_lm' or 'strategy_lmer'

Description

used in project p2901

Usage

model_analyse(
  pepIntensity,
  model_strategy,
  subject_Id = "protein_Id",
  modelName = "Model"
)

See Also

Other modelling: Contrasts, ContrastsMissing, ContrastsModerated, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, build_model(), build_models(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), isSingular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), make_model(), merge_contrasts_results(), model_summary(), moderated_p_limma(), moderated_p_limma_long(), my_contest(), my_contrast(), my_contrast_V1(), my_contrast_V2(), my_glht(), pivot_model_contrasts_2_Wide(), plot_lmer_model_and_data(), plot_lmer_peptide_noRandom(), plot_lmer_peptide_predictions(), plot_lmer_predicted_interactions(), strategy_lmer(), summary_ROPECA_median_p.scaled()

Examples

ionstar <- prolfqua_data('data_ionstar')$normalized()
ionstar$config <- old2new(ionstar$config)

ionstar$data <- ionstar$data |> dplyr::filter(protein_Id %in% sample(protein_Id,10))
prolfqua::table_factors(ionstar$data, ionstar$config)
formula_randomPeptide <-
  strategy_lmer("transformedIntensity  ~ dilution. + (1 | peptide_Id)")
mr <- model_analyse( ionstar$data,
 formula_randomPeptide,
 subject_Id = ionstar$config$table$hierarchy_keys_depth())
get_complete_model_fit(mr$modelProtein)

wolski/prolfqua documentation built on May 12, 2024, 10:16 p.m.